How to Use the Cliengo MCP in LlamaIndex

Q: How do I configure the Cliengo MCP Server for LlamaIndex?

Run pip install llama-index-tools-mcp. Set up a BasicMCPClient with your Vinkius URL, wrap it in McpToolSpec, and pass the async tool list to your FunctionAgent.

Q: Can LlamaIndex search through specific Cliengo contact messages?

Yes. The agent executes getcontactmessages, takes the raw output, and chunks it into your vector store for immediate semantic retrieval.

Q: Does the framework support filtering which tools are active?

You restrict access using the allowedtools parameter. If you only want the indexer to see listcliengousers, you just block the rest.

Index Cliengo chat histories and lead profiles directly into LlamaIndex for semantic search and RAG applications.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Cliengo MCP to LlamaIndex

Create your Vinkius account to connect Cliengo to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Cliengo with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build a LlamaIndex RAG pipeline for chat logs

Raw chat logs are useless if you cannot search them. By hooking this MCP Server into LlamaIndex, you pull thousands of interactions using `list_chat_conversations` and `get_chat_history` directly into your indexing pipeline. Those transcripts convert instantly into queryable vectors. When a sales rep asks about a specific objection, the engine retrieves the exact conversational context instead of generating a generic response.

Index your lead database

Stop treating prospect data as static text. Your indexing job runs `list_cliengo_leads` followed by `get_lead_details` to ingest every known attribute about your contacts into a central store. The resulting knowledge base lets you ask complex questions about your pipeline. LlamaIndex grounds its answers in the actual API data pulled from your conversational marketing campaigns.

Map internal configurations

Agency setups often involve dozens of widget deployments. You point a FunctionAgent at `list_cliengo_websites` and `list_cliengo_webhooks` to document your entire infrastructure. This creates a searchable map of where your chat tools live and how they route data. If someone asks which project lacks a real-time webhook, the system knows immediately.

Setup guide

Set up Cliengo MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Cliengo MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Cliengo tools.",
)
response = await agent.run("List recent Cliengo data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cliengo. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Cliengo now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Cliengo MCP in LlamaIndex

Run `pip install llama-index-tools-mcp`. Set up a `BasicMCPClient` with your Vinkius URL, wrap it in `McpToolSpec`, and pass the async tool list to your `FunctionAgent`.

Yes. The agent executes `get_contact_messages`, takes the raw output, and chunks it into your vector store for immediate semantic retrieval.

You restrict access using the `allowed_tools` parameter. If you only want the indexer to see `list_cliengo_users`, you just block the rest.

Your application needs to re-run the fetch operation. You schedule a job that pulls recent threads and updates the existing document vectors.

The MCP connection operates on a strict zero-trust model. When your pipeline requests raw customer transcripts or lead phone numbers, the Vinkius managed endpoint authenticates via a single token and leaves no persistent footprint behind.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript